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Understanding the Role of Temperature in Diverse Question Generation by GPT-4
We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions.
Accelerated Cloud for Artificial Intelligence (ACAI)
Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions.
A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education
While there is a growing body of research in computing education on utilizing large language models (LLMs) in generation and engagement with coding exercises, the use of LLMs for generating programming MCQs has not been extensively explored.
Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives
We evaluated 127 LOs that were automatically generated based on a carefully crafted prompt (detailed guidelines on high-quality LOs authoring) submitted to GPT-4 for conceptual modules and projects of an AI Practitioner course.
Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses
Additionally, we analyze the assessments that were not handled well by GPT-4 to understand the current limitations of the model, as well as its capabilities to leverage feedback provided by an auto-grader.
Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?
We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level.
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code
While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e. g., what is true/false about the snippet, or what is its output) appear to be the most challenging.
Improving Domain Independent Question Parsing with Synthetic Treebanks
Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks.
